1. Field of the Invention
The present invention relates to the field of information handling systems, more particularly to deploying information handling systems in a multi-server environment
2. Description of the Related Art
As the value and use of information continues to increase, individuals and businesses seek additional ways to process and store information. One option available to users is information handling systems. An information handling system generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes thereby allowing users to take advantage of the value of the information. Because technology and information handling needs and requirements vary between different users or applications, information handling systems may also vary regarding what information is handled, how the information is handled, how much information is processed, stored, or communicated, and how quickly and efficiently the information may be processed, stored, or communicated. The variations in information handling systems allow for information handling systems to be general or configured for a specific user or specific use such as financial transaction processing, airline reservations, enterprise data storage, or global communications. In addition, information handling systems may include a variety of hardware and software components that may be configured to process, store, and communicate information and may include one or more computer systems (including server systems), data storage systems, and networking systems.
One issue relating to computer systems relates to deploying computer systems in a multi-system environment. A multi-system deployment includes three phases, a deployment planning phase 110, a deployment operation phase 120 and a deployment transition phase 130 (See for example, FIG. 1, labeled Prior Art). The deployment planning phase 110 includes setting up an initial solution configuration based on capabilities of hardware, as well as developing and integrating automated delivery processes. The deployment operation phase 120 includes delivering a solution configuration to each server. The deployment transition phase 130 includes migrating existing solution configurations to new platforms as old platforms reach end-of-life or next generation systems become available as well as developing and integrating new automated delivery processes.
Known deployment methods often may carry a high fixed cost for each deployment phase and a high variable cost as the number of systems being deployed increases. Additionally, the cost of deployment continues to rise as systems become more heterogeneous within an environment, since automated delivery processes become more complex. Differences between server models also increase the risk of deployment errors and system misconfigurations.
Systems in a deployment set can vary from exactly identical to completely different. The amount of difference between each system determines how and when device configurations must be assigned (or “bound”) to system devices. Homogeneous deployments can use solutions that require early binding between the configuration and the systems (e.g., configuration data for a redundant array of independent disks (RAID) card should be determined during the planning phase.) Heterogeneous deployments can use deployment solutions that require late binding of configurations to devices. An example of late binding would be determining the configuration data for a RAID card on a per-system basis during the operation phase.
It is desirable to provide a method to deploy solution configurations to multiple heterogeneous systems in a simple, automated manner. A desirable solution would carry low fixed costs across each phase of the deployment process, and little or no variable costs as the number of systems increases.
There are a plurality of known methods of deploying multiple systems. These known methods can be classified into three categories, an automated deployment of same systems category (i.e., a homogeneous category), an automated deployment of implementation equivalents category (i.e., homogeneous with slight exceptions category), and a manual deployment of capability equivalents category (i.e., a heterogeneous category). Each of the categories provides associated advantages and disadvantages during the deployment lifecycle.
The automated deployment of same systems category deploys a solution configuration across homogeneous systems where servers are exactly alike (i.e., the same model, peripherals, peripherals located in same physical slots, chipsets and storage components). The deployment of same systems enables early binding of configurations to systems. With the automated deployment of same systems category, known automation processes and software can be used to reduce some variable costs. For example, an image from a source system may be captured and then this image may be deployed to multiple servers. However, with the automated deployment of same systems category, the initial system configuration should be developed (i.e., a source image should be created) and system particulars should be learned prior to deployment. Early binding requires that the configuration of system devices be fully expressed during the planning phase. Additionally, during the operation deployment phase early binding may cause any variance between systems to break the automated deployment process or corrupt the system configuration. Additionally, during the transition deployment phase development of a new system configuration (i.e., a new source image) is required when new systems are added.
The automated deployment of implementation equivalents category deploys systems with identical implementations where models are trivially different, peripherals are identical, chipsets are trivially different, and storage configurations are trivially different. The deployment of implementation equivalents requires early binding with exception handling during the deployment process. With the automated deployment of implementation equivalents category, automated processes and software can be used to reduce some variable costs. However, with the automated deployment of implementation equivalents category, development is required for the system configurations and the automated process. Early binding requires that the configurations be completely defined, as well as any exceptions between the systems. The process must be tested to make sure that the process accurately handles each exception. During the transition deployment phase new models would require that a new automated process be created. The costs associated with the planning and operation phases are duplicated during a transition. All new systems can only be trivially different from each other.
The manual deployment of capability equivalents category provides human transformation of a solution's configuration across heterogeneous systems. In a manual deployment of capability equivalents category, systems can vary by model, peripherals, peripheral's slot location and storage components, but the capabilities of each system should be exactly alike. This is a late binding of configurations to systems. With a manual deployment of capability equivalents category no automation development is required and system component vendors can vary. Configurations can be manually adapted to fit each system. Because of late binding, configurations can be defined during the operation phase. During the transition phase configurations can be manually ported to new systems without additional development. New systems can be completely different from each other, provided they each have a baseline for capabilities. Late binding can be used when defining configurations for each system. However, with the manual deployment of capability equivalents category, there can be a high cost associated with the planning, operation, and transition phases of deployment. The high cost is associated with the human, manual deployment of each individual system. System differences must be accounted for during each phase of the deployment process. The risk of error during every phase is high due to the human interaction involved.